Mcan-YOLO: An Improved Forest Fire and Smoke Detection Model Based on YOLOv7

被引:1
|
作者
Liu, Hongying [1 ]
Zhu, Jun [2 ]
Xu, Yiqing [2 ]
Xie, Ling [1 ]
机构
[1] Nanjing Univ Sci & Technol, Zijin Coll, Sch Comp & Artificial Intelligence, Nanjing 210023, Peoples R China
[2] Nanjing Vocat Univ Ind Technol, Sch Comp & Software, Nanjing 210023, Peoples R China
来源
FORESTS | 2024年 / 15卷 / 10期
关键词
forest fire smoke detection; YOLOv7; normalization-based attention module; CARAFE; AFPN;
D O I
10.3390/f15101781
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Forest fires pose a significant threat to forest resources and wildlife. To balance accuracy and parameter efficiency in forest fire detection, this study proposes an improved model, Mcan-YOLO, based on YOLOv7. In the Neck section, the asymptotic feature pyramid network (AFPN) was employed to effectively capture multi-scale information, replacing the traditional module. Additionally, the content-aware reassembly of features (CARAFE) replaced the conventional upsampling method, further reducing the number of parameters. The normalization-based attention module (NAM) was integrated after the ELAN-T module to enhance the recognition of various fire smoke features, and the Mish activation function was used to optimize model convergence. A real fire smoke dataset was constructed using the mean structural similarity (MSSIM) algorithm for model training and validation. The experimental results showed that, compared to YOLOv7-tiny, Mcan-YOLO improved precision by 4.6%, recall by 6.5%, and mAP50 by 4.7%, while reducing the number of parameters by 5%. Compared with other mainstream algorithms, Mcan-YOLO achieved better precision with fewer parameters.
引用
收藏
页数:22
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